Consolidating risk and profit/loss information for commercial investment banks is a big data problem. To accurately compute consolidated measures of risk and profit loss requires non trivial aggregation algorithms to be applied over large datasets. For the interest rates and currencies business of a major corporate or investment bank alone, these data sets are of the order of billions of records.
Current approaches increase the complexity with which algorithms are developed and deployed and further create a conflict between the optimum form for storing the data over the longer term, and the form in which the data is used at the point at which the aggregation algorithm is executed.
Other drawbacks may also be present.